[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-AGTFP":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"AGTFP","tag",[4],4,1,100,[11,71,115,168],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":16,"published_at":19,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":23,"score_detail":24,"sources":29,"tags":31,"primary_topic":38,"secondary_topics":44,"classification":45,"search_phrases":61,"slug":64,"view_count":65,"doi":16,"paper":66,"created_at":70},1002,"Digital villages and agricultural green total factor productivity: Evidence from a quasi-natural experiment in China","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37246876","原文标题: Digital villages and agricultural green total factor productivity。作者: Guo Hui、Wang Xinyi、Xia He、Jiang Wenjie。DOI: 10.3389\u002Ffsufs.2026.1831978。本文采用super-efficiency SBM-DDF-GML模型，运用2014—2023年中国县级面板数据测算AGTFP，并以国家数字乡村试点为准自然实验，运用DID方法评估数字乡村发展对AGTFP的影响、机制与空间溢出效应。研究发现：①数字乡村发展显著提升AGTFP，效应主要源于绿色技术进步而非绿色技术效率改善；②机制上通过优化劳动力配置、激发农业创业活力、扩大消费需求；③异质性上，距省会近、地形起伏度高、路网密度低的地区政策效应更显著；④空间效应以虹吸为主，但绿色技术进步随时间推移呈现溢出迹象。",null,"## Digital villages and agricultural green total factor productivity\n\n## Guo, HuiWang, Xinyi; Xia, He; Jiang, Wenjie\n\nAngol nyelvű Szakcikk (Folyóiratcikk) Tudományos\n\n*   SJR Scopus - Horticulture: D1\n\nEnhancing agricultural green total factor productivity (AGTFP) is essential for advancing high-quality agricultural development. This study employs a super-efficiency SBM-DDF-GML model to calculate AGTFP using county-level panel data from China (2014-2023). Leveraging national digital village pilot (NDVP) programs as a quasi-natural experiment, it further applies a Difference-in-Differences (DID) approach to evaluate the impact, underlying mechanisms, and spatial spillover effects of digital village development on AGTFP. Results indicate that digital village initiatives significantly boost AGTFP. The results remain robust across multiple robustness checks and endogeneity tests, with effects primarily driven by green technological progress rather than green technological efficiency improvements. Mechanism analysis indicates that digital village development enhances AGTFP by promoting optimal labor allocation, stimulating agribusiness entrepreneurship, and expanding consumption. Heterogeneity analysis reveals that the policy effect is more pronounced in areas near provincial capitals, with high terrain undulation, and low road network density. The spatial effect during the sample period is dominated by siphoning, with only green technological progress exhibiting signs of spillover over time. This study provides new empirical evidence on how digital village development drives agricultural green transformation and offers policy implications for optimizing policy design and promoting coordinated regional development.","Frontiers in Sustainable Food Systems 2026, 10, 1831978 · 2026-08-26","2026-08-26T00:00:00Z","论文",10,false,80,{"impact":25,"substance":25,"depth":26,"authority":27,"freshness":7,"relevant":8,"comment":28},22,20,12,"基于全国县级面板数据与准自然实验，实证数字乡村对农业绿色全要素生产率的影响，方法严谨，结论具有政策参考价值。",[30],{"name":18,"url":14},[32,4,33,34,35,36,37],"数字乡村","双重差分","县级面板","绿色全要素生产率","DID","Front Sustain Food Syst",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,56],{"code":39,"score":7,"supported":52,"title":53,"abstract":54,"tags":55},true,[],[32],[32],{"code":57,"score":8,"supported":22,"title":58,"abstract":59,"tags":60},"H4",[],[],[35],[62,63],"绿色全要素生产率 县级面板 双重差分 数字乡村","绿色全要素生产率 县级面板","绿色全要素生产率县级面板双重差分数字乡村-1002",7,{"doi":16,"openalex_id":16,"authors":67,"venue":16,"cited_by_count":68,"oa_url":16,"card":16,"direction":16,"ingested_from":69},[],0,"agent","2026-08-27T00:05:20.326038Z",{"id":72,"title":73,"url":74,"summary":75,"summary_zh":16,"content":76,"source_name":77,"source_url":16,"published_at":78,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":79,"score_detail":80,"sources":83,"tags":85,"primary_topic":16,"secondary_topics":89,"classification":90,"search_phrases":109,"slug":112,"view_count":113,"doi":16,"paper":16,"created_at":114},743,"中国农业新质生产力与绿色发展绩效的耦合协调度研究","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751","基于30省2013-2023面板,采用全球非径向VRS-SBM-GML模型测度农业绿色全要素生产率(AGTFP);耦合协调度从2013年0.3631升至2023年0.6072,新型劳动对象\u002F劳动资料维度率先进入初级协调,新型劳动力维度仍处勉强协调(0.5992)。","## 1. Introduction\n\nDeveloping agricultural new quality productive forces is an important pathway for shifting the drivers of agricultural development from conventional factor inputs toward scientific and technological innovation and optimized factor allocation, thereby improving the quality of agricultural development. Compared with agriculturally advanced countries, China still has considerable scope to improve agricultural labor productivity, the allocation of scientific and technological resources, and the commercialization of agricultural research outputs. Jiang [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B1-agriculture-16-01751)] estimated that, in 2020, agricultural labor productivity in the United States, Canada, and Israel was approximately 17.3, 19.8, and 25 times that of China, respectively. Meanwhile, increasingly stringent constraints on cultivated land, water resources, and the ecological environment have reduced the marginal returns of the conventional agricultural growth model, which relies primarily on expanding land, labor, machinery, and chemical inputs. A shift in development momentum through technological innovation, the reorganization of production factors, and changes in production modes is therefore urgently needed. The application of emerging technologies, including biological breeding, intelligent agricultural machinery, and artificial intelligence, has created new conditions for restructuring agricultural production functions and upgrading the form of agricultural productive forces, while drawing greater attention to the coordination between the upgrading of agricultural productive forces and resource–environmental performance.\n\nAgricultural new quality productive forces refer to an advanced form of agricultural productive forces led by agricultural scientific and technological innovation, grounded in improvements in worker quality, the modernization of the means of labor, and the optimization of the objects of labor, and formed through the reorganization of production factors and changes in production modes. They are characterized by high technology intensity, high efficiency, high quality, and greenness [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B2-agriculture-16-01751)]. Their “new quality” lies primarily in changes in factor quality, technological forms, and allocation patterns, rather than in a rejection of the basic components of conventional agricultural productive forces. Xi [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B3-agriculture-16-01751)] noted that green development is the defining feature of high-quality development and that new quality productive forces are inherently green productive forces, indicating an intrinsic consistency in the value orientation of the upgrading of productive forces and green development. Agricultural green development, in turn, emphasizes improving resource-use efficiency and reducing environmental burdens while maintaining agricultural output. The two concepts reflect the transformation of agricultural production modes from the perspectives of factor upgrading and resource–environmental performance improvement, respectively; however, their development levels and rates of evolution are not necessarily synchronized.\n\nIn recent years, China has made overall progress in agricultural green development. The composite agricultural green development index reported in the China Agricultural Green Development Report 2024 [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B4-agriculture-16-01751)] indicates that the national level of agricultural green development in 2023 was higher than both the previous year and 2015. Nevertheless, China’s agricultural green transition still faces practical constraints, including relatively low input-use efficiency. Data released by the Department of Science, Technology and Education of the Ministry of Agriculture and Rural Affairs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B5-agriculture-16-01751)] show that, in 2020, the fertilizer- and pesticide-use efficiencies of China’s three major grain crops were 40.2% and 40.6%, respectively. These indicators represent the shares of applied fertilizer nutrients absorbed by crops in the current season and of pesticides effectively used against target pests. Thus, although agricultural output has continued to grow, substantial room remains to improve resource-use efficiency and control environmental burdens. Whether the upgrading of agricultural productive forces can remain synchronized with improvements in resource–environmental performance has therefore become an important issue in transforming the agricultural development mode.\n\nExisting studies have examined the relationship between ANQPF and agricultural green development, but the coordination differences among the constituent dimensions of productive forces and the dynamic evolution of coordination states have not yet been systematically investigated. Because agricultural green development encompasses economic growth, resource use, ecological protection, social welfare, and governance institutions, this study does not construct a composite measure of agricultural green development in the broad sense. Instead, it focuses on the resource–environmental performance dimension and uses agricultural green total factor productivity (AGTFP) under resource and environmental constraints to represent AGDP. Against this background, the objective of this study is to characterize the temporal evolution, dimension-specific coordination, and state-transition patterns between ANQPF and AGDP across Chinese provinces. Specifically, using data for 30 Chinese provincial-level regions from 2013 to 2023, this study addresses three questions: (1) What annual evolutionary patterns characterize ANQPF and AGDP? (2) How does the coordination between AGDP and ANQPF as a whole, as well as its three constituent dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ? (3) How persistent are different coordination states, and in which directions do they transition?\n\n## 2. Literature Review\n\nResearch on agricultural green development has focused mainly on its conceptual connotation, influencing factors, and measurement methods. Existing studies generally define agricultural green development as the coordinated pursuit of agricultural economic growth, resource conservation, and ecological and environmental protection while ensuring an effective supply of agricultural products [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B6-agriculture-16-01751)]. Related research has further examined the determinants of agricultural green development from the perspectives of scientific and technological innovation, digital transformation, industrial restructuring, land-factor allocation, and agricultural socialized services, suggesting that regional resource endowments, technological foundations, and institutional conditions shape the effectiveness of the green transition [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B7-agriculture-16-01751)]. In terms of measurement, two principal approaches are used: composite indicator evaluation and green total factor productivity. The former captures the multidimensional objectives of agricultural green development, whereas the latter incorporates resource inputs, desirable outputs, and undesirable outputs into a unified production frontier and focuses on input–output performance under resource and environmental constraints [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B8-agriculture-16-01751),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B9-agriculture-16-01751),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B10-agriculture-16-01751)]. Because this study examines the coordination between the upgrading of productive forces and resource–environmental performance, AGTFP is used to represent AGDP, but it is not treated as equivalent to agricultural green development in the broad sense.\n\nAgricultural new quality productive forces are rooted in the Marxist theory of productive forces and represent a contextual development of this theory amid a new round of technological revolution and agricultural modernization. Workers, means of labor, and objects of labor remain the basic components of agricultural productive forces. ANQPF do not alter this fundamental logic; rather, they reflect the upgrading of factor quality, technological forms, and factor combinations under the leadership of scientific and technological innovation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B11-agriculture-16-01751)]. Compared with conventional agricultural productive forces, which depend largely on quantitative expansion of land, labor, machinery, and chemical inputs, ANQPF place greater emphasis on technological innovation, the application of digital technologies, and optimized factor combinations [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B12-agriculture-16-01751)]. Existing studies have mainly addressed conceptual definition, functional effects, and level measurement. They generally characterize ANQPF as an advanced form of productive forces led by scientific and technological innovation and formed through qualitative upgrading and optimized combinations of production factors, and they construct evaluation systems either around the three elements of productive forces or around functional dimensions such as technology, digitalization, and greenness [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B15-agriculture-16-01751),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751)]. Common measurement methods include the entropy-weight method, entropy-weight TOPSIS, and Markov chains. These studies generally find that ANQPFs in China have increased overall, although substantial regional disparities remain [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B17-agriculture-16-01751)].\n\nResearch on the relationship between ANQPF and green performance has followed two main analytical approaches: unidirectional effects and system coordination. Econometric studies primarily examine the effects of ANQPF on agricultural green production efficiency, carbon-emission efficiency, and sustainable-development performance, treating technological innovation, digital transformation, industrial upgrading, and factor optimization as potential channels [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B19-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)]. Other studies adopt a system-coordination perspective to investigate the relationships between new quality productive forces and agricultural green development, between digitalization and greening, and between ANQPF and green grain-production capacity. They generally find that coordination has improved, although regional disparities remain pronounced [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B21-agriculture-16-01751),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B22-agriculture-16-01751),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B23-agriculture-16-01751)]. Consistent with these findings, research on sustainable intensification, precision agriculture, digital agriculture, and agricultural innovation systems indicates that technological upgrading can improve resource use and environmental performance, but the extent to which these benefits materialize depends on infrastructure, human capital, service systems, and the institutional environment [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B24-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B27-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751),[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B29-agriculture-16-01751)]. The upgrading of agricultural productive forces and improvements in green performance are therefore not necessarily synchronized.\n\nOverall, the existing literature has three limitations. First, most studies focus on the unidirectional effect of ANQPF on green performance and pay insufficient attention to the synchronization and structural alignment of the two systems. Second, some studies include greening indicators in both ANQPF and agricultural green development assessments, leaving the measurement boundaries between the two systems insufficiently defined. Third, existing coordination studies primarily examine overall levels and spatial disparities, while giving limited attention to coordination differences among the three dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—or to the persistence, upgrading, and decline of coordination states. To address these gaps, this study uses AGTFP to represent AGDP, examines the coupling coordination between AGDP and both ANQPF as a whole and its three constituent dimensions, and applies a Markov chain to analyze the dynamic evolution of coordination states.\n\nThe marginal contributions of this study are threefold. First, AGTFP is used to represent AGDP, distinguishing this approach from composite assessments that incorporate economic, social, and ecological dimensions and thereby clarifying the measurement boundaries between ANQPF and AGDP. Second, the analysis extends from overall coordination to the three dimensions of new-quality means of labor, new-quality labor force, and new-quality objects of labor, allowing differences in the alignment between distinct elements of productive forces and AGDP to be compared. Third, in addition to examining coordination levels and spatial distributions, a Markov chain is introduced to identify the persistence, upgrading, and decline of coordination states. The coupling coordination degree reflects the synchronization, alignment, and coordination of the two systems; it is not used to identify the direction of influence or a causal relationship between them.\n\n## 3. Theoretical Analysis\n\nThis study draws on the theory of productive forces, system-coupling theory, and evolutionary and path-dependence theory to examine the relationship between ANQPF and AGDP at three levels: overall coordination, dimension-specific coordination, and state transitions. The purpose is to evaluate the synchronization and structural alignment of the two systems rather than to identify causal relationships among variables. Accordingly, the following theoretical propositions concern coordination states and their evolution and do not presuppose a causal direction.\n\n### 3.1. Coupling Coordination Between Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nSystem-coupling theory holds that the coordination state between systems depends on the relative alignment of their development levels, evolutionary rates, and internal structures. ANQPF and AGDP are jointly embedded in agricultural production and operation. New-quality means of labor, new-quality labor force, and new-quality objects of labor reflect qualitative upgrading in production conditions, production actors, and production objects, whereas AGDP captures changes in aggregate output under resource and environmental constraints. Changes in technological equipment, digital infrastructure, and human capital occur within the same production system as changes in agriculture-related environmental outputs. The development states of the two systems may therefore correspond, but they are not necessarily synchronized. Differences in technological suitability, resource endowments, and development stages may generate divergence in their levels and rates of change: some regions may accumulate new-quality factors rapidly while AGDP lags, whereas others may exhibit the opposite pattern [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B30-agriculture-16-01751)]. The coupling coordination degree measured in this study is intended to reflect the synchronization and alignment of the two systems rather than to identify causal effects. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 1****(H1).**\n\nANQPF and AGDP exhibit a general trend of joint improvement, but their growth trajectories are not fully synchronized; consequently, the overall level of coupling coordination increases over time.\n\n### 3.2. Differentiated Coordination Between the Dimensions of Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nThe theory of productive forces regards workers, means of labor, and objects of labor as the basic components of productive forces. ANQPF thus represent the qualitative upgrading and recombination of these three components driven by scientific and technological innovation [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B31-agriculture-16-01751)]. Because the foundations, evolutionary cycles, and regional dependence of the three dimensions differ, each may form a distinct coordination relationship with AGDP. New-quality means of labor reflect upgrades in technological equipment and digital infrastructure. Although technological renewal is relatively rapid, its diffusion is uneven, and its rate of change may be faster or slower than that of AGDP, producing different alignment states [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751)]. The new-quality labor force reflects the accumulation of human capital and skills. Its formation and allocation require longer periods, while the efficiency with which it is transformed into productive outcomes depends on training and extension systems [[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751)]; its coordination with AGDP may therefore differ from that of the means-of-labor dimension. New-quality objects of labor reflect changes in how production objects are developed and utilized. They are strongly dependent on resources and location [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)], and their optimization and value realization exhibit marked path dependence, generating coordination characteristics distinct from those of the other two dimensions. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 2****(H2).**\n\nThe coordination levels and evolutionary pathways between AGDP and the three dimensions of ANQPF—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ.\n\n### 3.3. Dynamic Evolution of Coupling Coordination States\n\nEvolutionary and path-dependence theory indicates that technologies, knowledge, and institutional rules are cumulative. System transformation evolves incrementally along established trajectories but may stagnate or regress in response to external shocks [[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B32-agriculture-16-01751)]. Coordination states are built on existing technological and institutional arrangements. Technology diffusion and human-capital development are generally long-term processes, making cross-stage transitions difficult to achieve in the short run. Interdependence between technologies and institutions creates path inertia, and transformation usually occurs through incremental reconfiguration [[27](https","MDPI Agriculture 16(16):1751 2026-08-15","2026-08-15T00:00:00Z",76,{"impact":26,"substance":25,"depth":81,"authority":27,"freshness":7,"relevant":8,"comment":82},18,"研究中国农业新质生产力与绿色发展绩效的耦合协调度，具有全国性政策意义，数据详实，但时效性稍差。",[84],{"name":77,"url":74},[32,86,4,87,88],"新质生产力","耦合协调度","30省",[],{"status":91,"locked":22,"version":47,"method":48,"evidence":92},"unclassified",{"candidates":93,"agricultural_evidence":52,"reason":108},[94,99,103],{"code":95,"score":8,"supported":22,"title":96,"abstract":97,"tags":98},"I4",[],[],[87],{"code":39,"score":8,"supported":22,"title":100,"abstract":101,"tags":102},[],[],[32],{"code":104,"score":8,"supported":22,"title":105,"abstract":106,"tags":107},"J2",[],[],[86],"缺少足够的主题证据",[110,111],"新质生产力 耦合协调度 数字乡村 30省","新质生产力 耦合协调度","新质生产力耦合协调度数字乡村30省-743",2,"2026-08-19T23:57:32.301681Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":16,"content":120,"source_name":121,"source_url":16,"published_at":122,"category":20,"cover_url":16,"hotness":123,"is_selected":22,"score":124,"score_detail":125,"sources":129,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":161,"slug":164,"view_count":165,"doi":166,"paper":16,"created_at":167},294,"智慧农业赋能农业绿色转型的逻辑、障碍与建议——基于技术革新视角","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Frich_html\u002F10.12133\u002Fj.smartag.SA202505010","顾雪微、赵向豪在《智慧农业(中英文)》2026年第3期发表《智慧农业赋能农业绿色转型的逻辑、障碍与建议——基于技术革新视角》论文。研究智慧农业技术应用对农业绿色全要素生产率(AGTFP)的影响,基于技术革新视角系统分析智慧农业赋能农业绿色转型的内在逻辑、现实障碍与政策建议。研究通过构建智慧农业技术应用对AGTFP的影响机制框架,引入农业数据作为新质生产要素,考察「智慧农业技术应用→农业数据要素积累→要素配置效率提升→AGTFP提升」的传导路径,识别不同区域、不同农业发展水平下智慧农业技术赋能效应的差异化特征。研究发现,智慧农业技术的采纳显著提升了农业绿色全要素生产率,但其赋能效应受区域数字基础设施、农业产业化程度、农民数字素养等门槛条件制约。研究指出推进智慧农业赋能农业绿色转型需坚持技术革新与制度创新双轮驱动,加快农业数据要素市场化配置,完善智慧农业技术标准与质量评价体系,构建跨区域协同推进机制,缩小区域间数字农业发展差距。","文章编号: 1756196937923-382175617\n\n文献标识码: A\n\n专刊--数字技术赋能与农业经济范式转型\n\n智慧农业赋能农业绿色转型的逻辑、障碍与建议——基于技术革新视角\n\n*    顾雪微 1, \n*    赵向豪 [](mailto:709007878@qq.com), 2\n*    {{custom_author.prefix_cn}} {{custom_author.name_cn}}  {{custom_author.suffix_cn}} {{custom_author.prefix_cn}} {{custom_author.name_cn}}  {{custom_author.suffix_cn}} {{javascript:window.splitNeed=false;}} {{javascript:window.splitNeed=true;}}[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fdevelop\u002Fmag_rich_cn\u002Fimg\u002Forcid.png)](https:\u002F\u002Forcid.org\u002F%7B%7Bcustom_author.orcid%7D%7D), {{javascript:window.splitNeed=true;}}[](mailto:{{custom_author.email}}), {{custom_author.xref}}, \n\n![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fdevelop\u002Fmag_rich_cn\u002Fimg\u002F2.png)\n[检索](javascript:;)\n\n[作者关系图](javascript:;)\n\n展开\n\n*   1. 新疆财经大学信息管理学院，新疆 乌鲁木齐 830012，中国\n*   2. 新疆财经大学经济学院，新疆 乌鲁木齐 830012，中国\n*   {{custom_aff}}\n\n {{custom_authorNote.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>作者简介: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>作者简介: \u003C\u002Fstrong>$1\")}} \n\n**作者简介:**顾雪微，硕士，讲师，研究方向为数据管理与分析预测。E-mail： [1048307092@qq.com](mailto:1048307092@qq.com)\n\n![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Frich_html\u002F10.12133\u002F%7B%7Bcustom_bio.imgUrl%7D%7D){{custom_bio.content.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>作者简介: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>作者简介: \u003C\u002Fstrong>$1\")}}\n\n**通信作者:**赵向豪，博士，副教授，研究方向为中国特色社会主义经济理论与农业经济问题。E-mail： [709007878@qq.com](mailto:709007878@qq.com)\n\n {{custom_authorNote.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>通信作者: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>通信作者: \u003C\u002Fstrong>$1\")}} \n\n**收稿日期：**2025-05-13\n\n**网络出版日期：**2025-08-26\n\n**基金资助**\n\n国家社会科学基金一般项目(22BGL165)\n\n{{custom_fund}}\n\nLogic, Impediments and Suggestion of Smart Agriculture-Enabled Green Transition in Agriculture: Based on the Perspective of Technological Innovation\n\n*    GU Xuewei 1,\n*    ZHAO Xianghao [](mailto:709007878@qq.com), 2\n*    {{custom_author.prefix_en}} {{custom_author.name_en}}  {{custom_author.suffix_en}} {{custom_author.prefix_en}} {{custom_author.name_en}}  {{custom_author.suffix_en}} {{javascript:window.splitNeed=false;}} {{javascript:window.splitNeed=true;}}[![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fdevelop\u002Fmag_rich_cn\u002Fimg\u002Forcid.png)](https:\u002F\u002Forcid.org\u002F%7B%7Bcustom_author.orcid%7D%7D), {{javascript:window.splitNeed=true;}}[](mailto:{{custom_author.email}}), {{custom_author.xref_en}} ,\n\n![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fdevelop\u002Fmag_rich_cn\u002Fimg\u002F2.png)\n[检索](javascript:;)\n\n[作者关系图](javascript:;)\n\nExpand\n\n*   1. The School of Information Management, Xinjiang University of Finance Economics, Urumqi 830012, China\n*   2. The School of Economics, Xinjiang University of Finance Economics, Urumqi 830012, China\n*   {{custom_aff}}\n\n {{custom_authorNote.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>First author: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>First author: \u003C\u002Fstrong>$1\")}} \n\n**First author:**biography:GU Xuewei, E-mail: [1048307092@qq.com](mailto:1048307092@qq.com)\n\n![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Frich_html\u002F10.12133\u002F%7B%7Bcustom_bio.imgUrl%7D%7D){{custom_bio.content.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>First author: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>First author: \u003C\u002Fstrong>$1\")}}\n\n**Corresponding author:**ZHAO Xianghao, E-mail: [709007878@qq.com](mailto:709007878@qq.com)\n\n {{custom_authorNote.replace(\u002F^\\s*\u003Cp( [^\u003C]*)?>\u002Fig,\"\u003Cp>\u003Cstrong>Corresponding author: \u003C\u002Fstrong>\").replace(\u002F^(?!\u003Cp)(.)\u002Fg,\"\u003Cstrong>Corresponding author: \u003C\u002Fstrong>$1\")}} \n\n**Received：**2025-05-13\n\n**Online：**2025-08-26\n\n**Fund**\n\nThe National Social Science Fund of China(22BGL165)\n\n{{custom_fund}}\n\n**Copyright**\n\ncopyright©2026 by the authors\n\n**【目的\u002F意义】** 现代农业发展通过采用智慧农业技术推进农业绿色转型发展，实现农业生产技术范式转型，增加农业的期望产出，降低农业的非期望产出，从而提升农业绿色全要素生产率。本文旨在对智慧农业技术革新赋能农业绿色转型开展系统研究，重点厘清其赋能农业绿色转型的内在逻辑，深入剖析存在的障碍，并提出可行的对策建议，以全面提升智慧农业技术革新对农业绿色转型的赋能效果，为农业绿色转型注入新的活力。 **【进展】** 系统探究了智慧农业技术革新赋能农业绿色转型的内在逻辑：从数据维度切入，构建多源异构的农业数据采集体系，夯实数据根基；从算法层面发力，借助智能决策重塑资源配置模式，实现精准调控；在设备领域推进，突破传统装备创新范式，提升生产效率；自生态视角着眼，构筑绿色生态监测体系，实现农业可持续发展。通过“数据-算法-设备-生态”的协同演进，构成智慧农业赋能农业绿色转型的创新逻辑。同时，研究发现智慧农业的技术革新赋能农业绿色转型面临诸多障碍，如核心能力缺失与技术落地场景的适配性危机、市场需求侧与供给侧的适用性错配、政策培育的协同缺失与标准体系结构性缺失等。 **【结论\u002F展望】** 提出如下对策建议：打破技术应用“最后一公里”，激发农业主体能动性；加速市场化进程，提升农业绿色资源配置效率；加强顶层设计，形成普惠型智慧农业新格局。\n\n**关键词：** 智慧农业;  农业绿色转型;  赋能逻辑;  赋能障碍;  农业数据;  新质生产力 [{{custom_kwd}}](javascript:;);\n\n**中图分类号：** F303.3  {{custom_clc.code}} ({{custom_clc.text}})\n\n顾雪微, 赵向豪. 智慧农业赋能农业绿色转型的逻辑、障碍与建议——基于技术革新视角[J]. 智慧农业(中英文), 2026, 8(3): 40-50.  DOI: [10.12133\u002Fj.smartag.SA202505010](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202505010)\n\nGU Xuewei, ZHAO Xianghao. Logic, Impediments and Suggestion of Smart Agriculture-Enabled Green Transition in Agriculture: Based on the Perspective of Technological Innovation[J]. _Smart Agriculture_, 2026, 8(3): 40-50.  DOI: 10.12133\u002Fj.smartag.SA202505010\n\n**[Significance]** The adoption of smart agricultural technologies has significantly accelerated the development of modern agriculture, emerging as a pivotal driver for green transition within the agricultural sector. By enabling a fundamental shift in agricultural production paradigms, these innovations not only enhance desirable agricultural outputs but also effectively reduce undesirable environmental outputs. This dual effect substantially contributes to the improvement of agricultural green total factor productivity (AGTFP). **[Progress]** A systematic investigation was undertaken into the pivotal role of smart agriculture technology innovation in driving the green transition within the agricultural sector. The analysis was meticulously structured across three sequential stages: Firstly, it elucidated the intrinsic mechanisms through which technological advancements serve as enablers for this green transition, detailing how innovations in agriculture technology translate into environmental sustainability gains. Secondly, it identified and categorized the existing implementation barriers that hinder the widespread adoption of smart agriculture solutions, providing insights into the challenges faced by stakeholders at various levels. The advancement of smart agricultural technologies facilitated a paradigm shift, moving from isolated, fragmented data systems to cohesive, interconnected ecological systems that support sustainable growth. The core technological logic underpinning this transition operates through a synergistic \"data-algorithm-equipment-ecology\" evolutionary trajectory: (1) Data infrastructure construction: Establishing comprehensive, multi-source heterogeneous agricultural data acquisition systems that collect, process, and analyze data from diverse sources, forming the foundational data assets necessary for the development and deployment of smart agriculture solutions. (2) Algorithmic decision-making: Developing sophisticated AI-driven resource allocation models that leverage big data analytics to enable precision management, optimizing resource use, and enhancing productivity through intelligent decision-making systems. (3) Equipment modernization: Creating technical closed loops characterized by a virtuous cycle of \"equipment upgrading-efficiency enhancement-cost reduction-environmental benefits\" through the implementation of automated production management and intelligent harvesting systems that minimize human intervention and maximize output. (4) Ecological optimization: Building circular systems for waste valorization, carbon footprint tracking, and ecological monitoring that facilitate the recycling of agricultural by-products, reduce greenhouse gas emissions, and promote the restoration and preservation of agricultural ecosystems, ultimately achieving self-regulating, resilient agricultural environments. However, the implementation process faces three categories of barriers: (1) Technological barriers: High infrastructure costs, inefficient technology commercialization, insufficient digital literacy among farmers, and inadequate full-process automation\u002Fintelligence levels constrain transformation depth. (2) Market barriers: Significant mismatches between supply-side capabilities and demand-side requirements in technology applicability. (3) Institutional barriers: Firstly, the lack of policy coordination is evident in the \"prioritizing construction over operation\" approach, leading to many agricultural big data platforms failing to effectively implement green technologies post-completion due to inadequate policies supporting sustained operations and practical applications. Secondly, structural deficiencies in the standard system, particularly the absence of unified classification standards for agricultural information resources, create technical barriers to cross-departmental and cross-regional data sharing. **[Conclusions and Prospects]** To effectively advance smart agriculture technology in driving the agricultural green transition and systematically address identified barriers, the following policy recommendations are proposed: (1) To effectively overcome the persistent \"last mile\" barrier that impedes the widespread adoption of technological innovations among smallholder farmers, it is imperative to actively promote multi-stakeholder participation across the agricultural sector. This involves fostering collaboration among governments, private enterprises, non-profit organizations, and local communities. Additionally, establishing a comprehensive technical service network is crucial, one that seamlessly integrates government guidance to ensure policy alignment, market operations to drive efficiency and sustainability, and social engagement to enhance community buy-in and knowledge dissemination. (2) To significantly boost the efficiency of green resource allocation within the agricultural sector, it is imperative to harness the power of market-oriented reforms. These reforms should serve as the cornerstone for forging a novel supply-demand-driven model that is both dynamic and responsive. By integrating intelligent algorithms and advanced data analytics, this coordinated approach will facilitate the precise matching of essential resources such as land, capital, and cutting-edge green technologies. Such precision will enable a paradigm shift from traditional, scale-oriented production methods to more flexible, demand-driven customization tailored to specific market needs. Ultimately, this transformation will cultivate sustainable endogenous growth momentum, providing a robust foundation for the agricultural sector's green transition and ensuring its long-term viability and competitiveness. (3) Governments ought to take the lead in driving systemic and comprehensive reform of existing policy formulation frameworks, ensuring that agricultural development strategies are aligned with long-term sustainability goals. Additionally, they should strengthen technological support systems for agricultural resource zoning, leveraging advanced tools such as big data analytics and geographic information systems to optimize land use and resource allocation. By doing so, governments can foster a new universal smart agriculture development paradigm that integrates digital innovation with ecological principles. These targeted measures will not only enhance agricultural productivity but also accumulate valuable experience for comprehensively advancing the agricultural green transition and promoting rural ecological revitalization in a systematic manner.\n\n### **{{custom_notes.title}}**\n\n {{custom_notes.content}} \n\n农业绿色转型作为新质生产力在农业领域的具体体现，其“新”的关键在于创新驱动，“质”的锚点在于高质量发展［1］。具体而言，农业绿色转型，指的是农业在绿色发展理念引领下，以绿色技术为核心驱动力，逐步从以往单纯追求经济增长的传统农业模式，转变为“减排”与“增效”协同共进、生态效益与经济效益并驾齐驱的新型农业发展形态的过程。\n\n智慧农业又称数字农业或信息农业［2］，是将大模型、物联网、大数据等技术运用到农业中，通过传感器采集数据，利用移动平台或电脑平台对农业生产进行控制，实现农业生产的精准化、智能化和高效化。它涉及农业信息快速获取、农田种植、土地管理、农药利用、污染控制、农业工程设备及其产业化技术等多种农业高新技术，是中国由农业大国迈向农业强国的必经之路。随着2017年提出的《关于创新体制机制推进农业绿色发展的意见》和2018年提出的《乡村振兴战略规划（2018—2022年）》，中央和各部门连续提出了一系列重要举措发展智慧农业，在持续关注农业绿色转型的同时，不断深化数字化绿色化协同转型发展，实现数字化赋能现代农业绿色化转型，支撑农业现代化建设［3］。相关政策文件如图1所示。\n\n![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Farticle\u002F2026\u002F2096-8094\u002F22558\u002F2096-8094-2026-8-3-40\u002Fthumbnail\u002F66C62C93-8750-4836-B2E0-481125182871-F001.jpg)\n\n##### **图1 2017—2025年提出的关于发展智慧农业和推动农业绿色转型的相关政策文件**\n\nFig. 1 Policy documents pertaining to smart agriculture development and agricultural green transition promulgated from 2017 to 2025\n\n在此背景下，根据马克思生产力和生产关系矛盾运动规律，新型生产力的发展需要与之相适应的新型生产关系［4］。现代农业发展通过采用智慧农业技术推进农业绿色转型发展，实现农业生产技术范式转型，增加农业的期望产出，降低农业的非期望产出，从而提升农业绿色全要素生产率。智慧农业通过突破传统生产边界，实现降耗增效与生态保护的协同发展，为农业绿色转型提供了强有力的技术支持。据此，从本体论、认识论、方法论［5］三个层面阐释智慧农业技术革新的内涵，这些创新如何使智慧农业成为农业绿色转型的关键驱动力，也是实现农业高质量发展与生态文明建设深度融合的必由之路。\n\n从本体论视角审视，农业绿色转型的全环节覆盖［6］包括：（1）农业空间布局绿色转型。遵循“因地制宜、宜农则农”，科学规划农业空间布局，适配资源环境承载力，优化产业结构，构建生态友好型布局体系。（2）农业资源利用绿色转型。以水土保持为核心，推广节水灌溉、提升耕地质量等技术，强化综合生产能力；推进农业废弃物资源化利用，构建资源节约型路径。（3）农业生产手段绿色转型。加强农业基建，推进农机装备绿色化、智能化改造；集成推广绿色生产技术，加快智慧农业应用，构建智慧化绿色生产体系。（4）农业产业链绿色转型。以全产业链转型为目标，推动农村一二三产业融合，发展精深加工、冷链物流等业态，延伸绿色产业链，提升附加值。（5）农产品供给绿色转型。对接消费升级，推动农产品供给优质化、多元化、营养化；完善质量标准体系，构建全链条信息化监管网络，保障绿色供给。（6）农产品消费绿色转型。强化绿色食品等认证，探索“碳标签”机制；提高全民健康与环保意识，提升绿色低碳产品市场占有率，倡导绿色消费方式。（7）农业生态环境绿色转型。聚焦减污降碳、资源利用与生态修复，通过防治农业面源污染、推广化肥农药减量增效、发展种养循环、提升耕地质量及保护生物多样性，构建绿色低碳农业体系［7］。智慧农业的技术革新以农业对象、农业环境、生产过程的三维数字化建模为基础，构建起覆盖农业空间布局、农业资源利用、农业生产手段、农业产业链、农产品供给、农产品消费、农业生态环境全环节的绿色转型数字化系统，重构传统农业的生产模式，实现农业系统要素的可视化表达、状态参数的实时采集与动态分析，进而通过数据融合与智能算法形成决策支持，最终达成农业生产系统的精准化调控与智能化优化，为破解农业高碳发展困局提供了系统性解决方案。\n\n从认识论视角审视，面对全球气候变化、资源约束与生态压力的多重挑战，农业绿色转型已超越单一环节改良，演变为涵盖经济、生态、社会多维目标的系统性变革［8］。绿色技术作为这场变革的核心驱动力，通过构建“感知-传输-分析-决策-执行”的智慧农业闭环，实现了农业生产函数的根本性重构。这种重构不仅体现为物联网设备、大数据分析等工具层面的技术升级，更深层次地推动着传统农业从经验驱动的边际改进向数据驱动的范式突破跃迁。当数字孪生技术赋予农业系统自我学习与优化能力时，农业生产开始呈现指数型增长特征——技术、组织、制度三大要素形成协同创新网络，推动农业从自然再生产过程向数据再生产与自然再生产深度耦合的新型生产方式演进［9］。这种转变标志着人类对农业系统的认知与改造进入新阶段，在实现资源高效利用、生态环境保护与经济价值创造的动态平衡中，绿色技术正引领农业绿色转型迈向量子跃迁式的创新发展路径。\n\n从方法论来看，农业绿色转型的理论研究正逐步深化其内涵建构，核心聚焦于“转型路径”的系统性探索。学者基于农业多功能性视角［10］，重新界定绿色转型的复合价值内涵；在技术创新维度［11］，构建资源高效利用与环境友好的技术解决方案；从竞争策略视角出发［12］，提出市场导向的绿色转型战略选择；在宏观驱动层面，依托低碳带动视角［13］，设计政策引导与碳减排协同的转型机制。智慧农业作为现代农业技术集成的核心平台，正在农业领域掀起一场深刻的变革。在信息感知方面，通过遥感卫星、无人机和地面传感器等设备，可以实现对农田环境的实时监测和数据分析，为农业生产手段绿色革新提供科学依据。在智能决策方面，利用大数据分析和机器学习技术，可以对农田环境、作物生长周期和农业资源进行深入挖掘和分析，为农业资源利用绿色转型提供科学的决策支持。在精准作业方面，通过集成先进的自动化设备和机器人技术，可以实现农田作业的智能化和自动化，提高农业绿色生产效率和质量。\n\n综上所述，智慧农业的技术革新为农业的绿色转型提供了新的思路和解决方案。本研究将深入探讨智慧农业的技术革新如何赋能农业绿色转型，系统分析其内在逻辑，深入剖析存在的障碍，并提出可行的对策建议。\n\n本研究遵循“系统探究-障碍剖析-对策建议”的递进式研究方法，基于研究目标与内容，研究思路如图2所示。\n\n![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Farticle\u002F2026\u002F2096-8094\u002F22558\u002F2096-8094-2026-8-3-40\u002Fthumbnail\u002F66C62C93-8750-4836-B2E0-481125182871-F002.jpg)\n\n##### **图2 智慧农业赋能农业绿色转型的总体研究思路**\n\nFig. 2 The overarching research framework for smart agriculture-enabled green transition in agriculture\n\n农业绿色转型涵盖农业空间布局、农业资源利用、农业生产手段、农业产业链、农产品供给、农产品消费、农业生态环境等环节。而智慧农业的技术革新通过数据要素渗透、智能装备替代、全链协同创新，重构了农业绿色转型的技术底座与商业模式，推动了从数据孤岛到生态闭环的技术突破。其技术逻辑本质上是利用“数据-算法-设备-生态”的协同进化，将农业生产的“经验依赖”转化为“数据智能驱动”，在提升效率的同时构建生态正循环，为农业绿色转型提供可量化的技术实现路径，如图3所示。\n\n![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Farticle\u002F2026\u002F2096-8094\u002F22558\u002F2096-8094-2026-8-3-40\u002Fthumbnail\u002F66C62C93-8750-4836-B2E0-481125182871-F003.jpg)\n\n##### **图3 智慧农业赋能农业绿色转型的技术逻辑**\n\nFig. 3 The technical mechanisms of smart agriculture enabling the green transition in agriculture\n\n在新一轮技术革命和产业变革的背景下，智慧农业将数据要素融入农业绿色生产环节，对于改造传统农业、转变农业发展方式具有重要意义。第一，农业现代化的新基建逻辑。在数字经济时代，数据已成为继土地、劳动力、资本、技术之后的第五大生产要素［14］。智慧农业的技术革新首先聚焦于数据层的重构，将农业绿色转型理念深度嵌入其技术体系。其逻辑起点在于农业信息论［15］与生态经济学的交叉融合。通过构建多源异构数据采集体系，依托多种绿色技术嵌入环境监测，实现农业活动数字孪生与绿色映射，解决数据采集痛点，构建含生态参数的数字底座，为决策优化提供支撑。第二，农业数据的复杂性管理。农业数据多源异构源于生产系统开放及与自然生态系统耦合。智慧农业突破数据采集体系兼容性与内化绿色转型的目标，采用低功耗广域网络（Low-Power Wide-Area Network，LPWAN）技术，开发太阳能供电模块降低能耗。通过标准化与语义对齐技术，嵌入生态约束条件，构建多模态融合模型，实现数据融合与互操作。如农作物长势监测中，通过深度学习历史种植数据、气象信息、土壤墒情等多维农业要素，智慧平台可精准预测病虫害风险、产量波动及市场趋势，为农户提供科学种植方案。表1汇总了智慧农业的典型应用场景及实践效益，直观展现了数据要素对农业绿色转型的赋能效果。\n\n###### **表1 智慧农业赋能农业绿色转型的典型应用案例及效益分析**\n\nTable 1 Smart agriculture-enabled agricultural green transition： exemplary implementation cases and benefit analytics\n\n| 技术方案 | 实际案例 | 实施模式 | 实施效果 |\n| --- | --- | --- | --- |\n| 人工智能+水肥一体化 | 北大荒集团鹤山农场 | 以智慧农业全国重点实验室项目为依托，通过模型计算生成变量施肥处方图；通过水肥一体化系统控制中心的云计算，实现分区按需自动灌溉和施肥，形成了田间农事信息“采、传、算、用”的全链路数据处理、执行模式 | 水肥利用率提高到60%，减少化肥用量5%，粮食增产8% |\n| 人工智能+农作物害虫防控 | 江苏姜堰井贤农场示范基地 | 平台可以通过输入作物、病种和环境信息，实现精准响应和无人机施药参数的指导 | 水稻区农药滥用率下降20% |\n| 人工智能+农业决策 | 巨野县太平镇 | 通过对气象、土壤、市场数据的深度分析，农户依据精准气象预警，提前采取保温措施，守护麦苗安全 | 收集气象环境、土壤养分、虫情、病害、作物长势等8大类信息，为绿色农事决策提供数据支撑 |\n| 人工智能+农情服务 | 巨野县核桃园镇 | 依托“作物云-农田遥感监测系统”，农户们精准掌握小麦生长动态，科学开展穗期病虫防控和抗旱减灾工作 | 小麦生育期监测分布图、干旱诊断图清晰呈现，让农业绿色生产管理更加精准高效 |\n| 人工智能+多光谱无人机 | 潍坊市安丘市石堆镇岳家官庄村的智慧化示范田 | 用智能传感装置构建数据采集网络，通过中化现代农业技术服务平台（Modern Agriculture Platform， MAP）智能算法模型，生成“一田一策”的种植方案 | 化肥用量直降20%、麦苗均匀度提升超10% |\n| 人工智能+麦田管理 | 南阳市新野县施庵镇的高标准农田 | 通过收集卫星影像、无人机航拍，并进行光谱分析、气象站实时监测、土壤墒情等数据，传回大数据处理中心，由农业专家提出农事精细化管理方案，新型喷灌机-指针式喷灌机进行作业 | 亩均节水120 m 3，节水率达到48% |\n\n智慧农业利用算法技术构建“数据解析-模型构建-决策优化-绿色效应”的闭环，通过提升农资供应水平、增强农资精准利用效率，将农业经验决策转化为人工智能智能驱动，进而打破农业绿色转型桎梏，实现农业资源利用绿色转型。\n\n第一，农业知识的数字化解构。农业经验决策的本质是模糊逻辑与隐性知识的集合，而数据解析的核心任务是通过算法将非结构化经验转化为结构化数据。基于卷积神经网络（Convolutional Neural Network, CNN）［16］与循环神经网络（Recurrent Neural Network, RNN）的混合架构，系统可优先识别资源消耗节点，解析农业数据的时空特征。引入贝叶斯网络（Bayesian Network）［17］构建农业知识图谱，将高温高湿导致病害暴发等经验规则转化为概率图模型。采用模糊逻辑（Fuzzy Logic）［18］处理农业数据的模糊性，将土壤肥力中等模糊表述转化为隶属度函数，通过模糊推理系统生成施肥建议，使决策更符合农业绿色生产的实际情境。\n\n第二，农业系统的认知建模。模型构建是算法层的核心，其目标是通过数学建模实现对农业系统的认知映射。智慧农业综合运用机器学习模型，应用于精准种植决策、病虫害绿色防控、碳足迹与生态优化、供应链与市场优化等场景。例如，长短期记忆网络（Long Short-Term Memory, LSTM）模型预测作物需水量以优化灌溉计划，XGBoost回归优化物流路径，ResNet34改进提升病虫害识别准确率，强化学习动态调整灌溉策略，迁移学习减少病虫害识别模型训练数据量。智慧农业利用算法逻辑解构农业知识体系，借助认知建模实现农业系统的创新性重构，通过闭环控制实现数据到行动的转化，最终通过精准资源利用达成农业可持续发展的目标，推进农业绿色转型进程。这场算法驱动的农业革命，标志着人类从征服自然的工业化思维，向与自然和谐共生的生态文明思维转变。\n\n智慧农业通过自动化生产管理、智能化机械收割推动农业生产手段绿色转型。其核心逻辑在于构建“设备升级-效率提升-成本降低-环境友好”的技术闭环，实现农业生产与生态保护的协同发展。\n\n第一，农业装备的智能化跃迁。传统农业装备的本质是能量转换工具，其功能局限于机械力的放大与传递。智慧农业基于大模型技术中计算机视觉与多传感器融合，开发适应复杂农田环境的无人驾驶农机，实现厘米级路径规划与避障，并搭载土壤成分传感器与智能算法，实时分析土壤湿度、肥力数据，动态调整耕作参数，减少化肥、农药过量使用，降低碳排放。同时，利用大模型技术和机器人技术，研发出适用于不同农产品的自动化采摘设备，通过精准识别与轻柔操作减少机械损伤，降低果实腐烂率。如合肥市长丰县草莓采摘机器人，配备高分辨率摄像头与高精度机械手臂，通过视觉识别技术，准确判断作物成熟度与位置［19］。\n\n第二，生产函数的重构与优化。设备升级直接提升农业生产效率，通过精准作业减少化肥、农药等农资过量使用，降低环境污染，突破传统生产函数边际报酬递减规律。智能设备使要素投入的边际产出持续提升，并突破人类劳动生理限制，实现全天候环境友好型作业与多任务并行处理。\n\n第三，全生命周期的经济性革命。设备层技术的成本降低效应不仅体现在直接人力成本节约，更在于全生命周期的成本优化。江苏省吴江区通过机器人除草、覆膜插秧、侧深施肥等绿色新质生产力技术，赋能形成集约节约、循环高效的绿色生态型无人农场发展新模式［20］。当拖拉机进化为移动的超级计算机，采摘臂升级为精准的生物工程师，农业便从体力密集型产业蜕变为技术密集型产业。这场设备驱动的农业革命，标志着人类从依赖自然的农耕文明，历史性地向驾驭自然的智能文明跨越。\n\n智慧农业通过废弃物资源化、碳足迹追踪等生态优化措施，构建“资源循环-碳减排-生态监测”的闭环，实现农业生态系统的自我调节与持续优化，从而推动农业绿色转型。\n\n第一，农业生态系统的物质流重构。资源循环的本质是打破传统农业“资源-产品-废弃物”的线性代谢模式，构建“资源-产品-再生资源”的闭环系统。通过生物转化、热化学转化等技术，将农业废弃物转化为高值化产品。智慧农业通过在线分析粪便成分，精准调控发酵参数，加速腐熟过程，提升有机肥质量，并改造废旧水泥窑生产高效矿物质肥料，实现工业废弃物的资源化利用。基于物质流分析（Material Flow Analysis, MFA）理论，构建农业系统的物质代谢模型，系统可追踪化肥从生产，到施用，再到作物吸收的全过程，通过氮素利用效率指标量化资源浪费［21］。\n\n第二，农业气候责任的主动担当。农业既是碳源也是碳汇，其碳减排的紧迫性源于全球气候变化的压力。在碳足迹追踪上，通过追踪农产品从生产到消费的全流程碳排放，生成碳足迹报告，结合气象数据与土壤湿度传感器，动态调整灌溉与施肥方案，减少资源浪费［22］。此外，智慧农业利用AI无人机与图像识别，监测农田周边鸟类、昆虫活动，评估生态影响，并实时监测水体、土壤中的重金属与农药残留，提前预警污染风险。例如，新疆昌吉棉花碳足迹试点搭建物联网碳监测体系，借助传感设备与AI无人机采集全流程种植数据，自动核算棉花碳足迹。依托监测数据推行减肥节水、秸秆炭化技术，大幅降低农田碳排放，氮肥施用减少25%［23］。\n\n第三，农业生态安全的守护机制。生态监测是农业绿色转型的“眼睛”，其核心价值在于提供实时、精准的生态状态信息。基于遥感、物联网、无人机等技术，构建“空天地一体化”监测体系，卫星遥感可监测区域植被覆盖度，无人机可捕捉田间微地形变化，地面传感器可实时监测土壤质量。引入生物完整性指数（Index of Biotic Integrity, IBI）［24］、土壤质量指数（Soil Quality Index, SQI）［25］等指标，构建农业生态健康评估框架，系统可通过分析昆虫多样性、微生物群落结构等参数，评估农田生态系统的稳定性。基于机器学习算法构建生态风险预测模型，系统可提前预测病虫害暴发概率，并生成生物防治、物理防控等综合方案，减少化学农药的使用。这一过程不仅体现了技术进步的力量，更揭示了农业作为生态系统的本质属性。当秸秆转化为肥料，当碳汇成为资产，当监测数据守护生态安全时，农业便实现了生态环境层面的绿色转型。\n\n中国农业绿色转型虽已取得初步成效，但在向纵深推进的征程中，仍面临技术、市场与体制机制层面的多重掣肘。在“大国小农”的国情农情背景下，传统生产方式向智慧化、绿色化转变的复杂性不容小觑，智慧农业技术的适配性与推广普及面临着严峻挑战。智慧农业作为农业强国和“双碳”目标［26］两大战略目标的关键引擎，其深度融合应用与全方位推广，尚有诸多难题亟待攻克［27］。由此可见，借助智慧农业提升中国农产品绿色竞争力的任务艰巨，农业绿色转型之路依旧任重道远。\n\n在智慧农业发展初期，农业基础设施投入成本过高、技术成果转化效率低下、农民对数字农业模式认识程度不足等限制无法得到有效破解；智慧农业全流程信息化、自动化、智能化程度不足在一定程度上桎梏着农业绿色转型的深度［28］。\n\n一是高端传感器与智能芯片的“卡脖子”困境［29］。农业传感器作为数据采集的神经末梢，其技术成熟度直接决定数据质量。中国传感器产业快速增长，应用模式也日渐成熟。目前，中国传感器产品约6 000种，而国外已达20 000多种，远远满足不了国内市场需求。中高端传感器进口占比达80%，传感器芯片进口更是达90%，国产化缺口巨大。其中数字化、智能化、微型化等高新技术产品严重短缺。国家重大装备所需高端产品主要依赖进口，生物信息传感器产品，如作物生长信息传感器、畜牧养殖信息传感器、水产养殖信息传感器几乎都来自国外企业，中国相关产品仍处于研发和试用阶段，真正市场化的产品不多，使得农业绿色转型进展缓慢［30］。\n\n二是算法黑箱与决策信任危机。技术可解释性是农业技术推广的关键。首先，大多数大数据模型的决策逻辑呈现“黑箱”特性，缺乏面向农业生产者的可视化解释路径。农户的传统经验与大数据决策冲突，农户对预测结果的信任度不足，间接制约了绿色技术推广效率，限制了农业生产方式的绿色转型［31］。其次，智慧农业虽被寄予了精准减量的期待，但在实践中却陷入技术增量不减量的悖论。农业面源污染已成为水体富营养化与土壤退化的主要诱因，当前，中国农业化肥利用率仅为40.2%［32］，导致大量氮、磷元素及化学残留物通过地表径流进入水体。更严峻的是，数字技术的硬件依赖可能加剧污染风险，如无人机植保作业因缺乏药液漂移的控制算法，对邻近田地造成损伤。\n\n三是小农户场景的","智慧农业(中英文) 2026, 8(3): 40-50","2026-08-05T00:00:00Z",25,75,{"impact":81,"substance":26,"depth":81,"authority":126,"freshness":127,"relevant":8,"comment":128},14,5,"核心期刊论文，系统阐述智慧农业赋能绿色转型的逻辑与障碍，具有学术深度，但时效性一般。",[130,131],{"name":121,"url":118},{"name":132,"url":133},"智慧农业(中英文) 2026年第3期","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F567288.html",[135,4,136,137,138],"智慧农业","智慧农业期刊","农业绿色转型","技术革新",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":142},{"candidates":143,"agricultural_evidence":52},[144,151,157],{"code":145,"score":146,"supported":52,"title":147,"abstract":148,"tags":150},"I1",15,[135],[135,149],"数字农业",[135],{"code":39,"score":152,"supported":52,"title":153,"abstract":154,"tags":156},3,[],[155],"数据要素",[],{"code":57,"score":8,"supported":22,"title":158,"abstract":159,"tags":160},[],[],[137],[162,163],"农业绿色转型 智慧农业期刊 技术革新 智慧农业","农业绿色转型 智慧农业期刊","农业绿色转型智慧农业期刊技术革新智慧农业-294",8,"10.12133\u002Fj.smartag.sa202505010","2026-08-07T23:57:51.274027Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":16,"content":173,"source_name":174,"source_url":16,"published_at":175,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":176,"score_detail":177,"sources":179,"tags":181,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":206,"slug":209,"view_count":152,"doi":210,"paper":16,"created_at":211},222,"数字乡村发展与农业绿色全要素生产率——来自中国的证据","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffsufs.2026.1746394\u002Ffull","基于中国30个省份2012-2022年面板数据,构建包含数字基础设施、数字服务能力和数字发展环境的多维评价框架,采用固定效应模型、中介模型和面板阈值模型考察数字乡村发展对农业绿色全要素生产率的影响。结果表明数字乡村发展显著提升AGTFP;机制分析显示数字化通过促进劳动力流动、扩大规模化经营、加强合作社发展、加快机械化与农业研发提升绿色效率;土地流转的正面效应受制度摩擦制约;阈值分析显示数字基础设施的影响在跨越临界水平后更强。","## Abstract\n\nThis study investigates how digital rural development influences agricultural green total factor productivity (AGTFP) in China, with particular attention to stage characteristics and regional heterogeneity. Using panel data from 30 provinces from 2012 to 2022, we construct a multidimensional evaluation framework incorporating digital infrastructure, digital service capacity, and the digital development environment. A fixed-effects model is employed to estimate the overall impact, mediation models are used to examine the roles of factor allocation, organizational upgrading, and technology diffusion, and a panel threshold model is applied to identify nonlinear effects. The results show that digital rural development significantly enhances AGTFP, and this finding is robust to alternative measures, sample adjustments, and endogeneity tests. Mechanism analyses reveal that digitalization improves green efficiency by promoting labor mobility, expanding large-scale operations, strengthening cooperative development, and accelerating mechanization and agricultural R&D. However, the positive effect of land transfer remains constrained by institutional frictions, limiting its contribution to green transformation. Threshold analyses indicate that the impact of digital infrastructure becomes stronger once a critical level is surpassed, whereas the marginal effect of digital services weakens at higher stages of development. Regional heterogeneity further shows that the positive effects are most pronounced in eastern provinces and in non-grain-producing regions. Overall, digital rural development functions as a multidimensional driver of agricultural green transformation, offering empirical evidence and policy insights for designing differentiated digitalization strategies that support sustainable agricultural development.\n\n## 1 Introduction\n\nThe rapid expansion of the digital economy in recent years has substantially advanced rural digitalization in China. Nationwide, the targets of “gigabit county connections, 5G coverage in every township, and broadband access in every village” have largely been achieved. By 2024, over 90% of administrative villages had access to 5G networks, and rural internet penetration reached 63.8%. Meanwhile, digital applications—such as online public services, smart agriculture, and digital breeding—have become deeply embedded in rural production and governance. Globally, digital transformation is recognized as a key pathway toward sustainable food systems. From the perspective of the resource-based view, data have emerged as a new strategic resource that enhances productivity through digital technologies. The widespread adoption of these technologies in agriculture provides new opportunities to overcome resource and environmental constraints and to accelerate green transformation (; ).\n\nHowever, China’s progress in agricultural green development remains limited. The excessive use of fertilizers and pesticides continues to cause non-point source pollution, resulting in a relatively low level of agricultural green total factor productivity (AGTFP) (). This paradox—rapid rural digitalization yet lagging green transformation—raises a critical question: can digital rural development truly empower agricultural green transformation?\n\nPrevious studies provide preliminary evidence that rural digitalization enhances agricultural efficiency (; ; ). Yet, most research has focused on measuring the level of digital village development or assessing its direct effects on productivity, with limited exploration of the internal mechanisms and regional heterogeneity. Whether digitalization improves AGTFP depends on several empowerment channels, including the reduction of transaction costs, facilitation of factor mobility, upgrading of organizational structures, and diffusion of green technologies. These mechanisms may vary across development stages and regions due to differences in infrastructure, economic conditions, and environmental pressures.\n\nTo address these gaps, this study examines how digital rural development affects AGTFP, whether such effects are stage-dependent or region-specific, and through which mechanisms they operate. Using provincial panel data from 2012 to 2022, we integrate mediation and threshold models to identify key empowerment pathways and nonlinear characteristics. The findings provide theoretical insights and practical implications for promoting sustainable and regionally balanced agricultural digitalization.\n\n## 2 Literature review and theoretical framework\n\n### 2.1 Literature review\n\n#### 2.1.1 Digital economy and sectoral development\n\nThe digital economy, characterized by data as a key production factor and digital technologies as enabling tools, has been widely recognized as a fundamental driver of efficiency improvement and structural transformation across multiple sectors (; ). Existing studies consistently show that the digital economy reshapes traditional production and governance systems by reducing information asymmetry, optimizing resource allocation, and accelerating technological innovation, thereby exerting broad economic, environmental, and social impacts (; ; ; ). Similar cross-sectoral effects of the digital economy have also been documented in the tourism sector, where digitalization promotes growth through online attention allocation and transportation infrastructure, as well as in rural human settlement improvement, highlighting its role in enhancing living environments beyond purely economic outcomes (; ; ).\n\nFrom the perspective of environmental sustainability, a growing body of literature demonstrates that digital economy development significantly contributes to green outcomes. Empirical evidence from China indicates that the digital economy reduces carbon emissions and enhances carbon efficiency by promoting green technological innovation and upgrading industrial structure, with these effects exhibiting clear nonlinear characteristics and regional heterogeneity. Similar findings are reported in studies on energy systems, where digitalization alleviates energy poverty primarily through innovation-driven channels, and the positive effects only emerge after surpassing certain development thresholds (). These results highlight that the environmental dividends of the digital economy are neither automatic nor uniform, but depend critically on development stages and structural conditions.\n\nBeyond environmental outcomes, the digital economy has also been shown to exert profound influences on social governance and public service provision. Recent studies focusing on rural China reveal that digital economy development improves public security by increasing non-agricultural employment opportunities and household income, thereby indirectly enhancing social stability (). In the public health domain, interprovincial evidence suggests that the digital economy significantly raises medical and health service levels through improved resource allocation efficiency and coordinated regional development, while generating notable spatial spillover effects (). Together, these findings underscore that the digital economy functions as a systemic force that reshapes factor mobility, institutional performance, and governance capacity across sectors.\n\nMoreover, quasi-natural experimental studies further strengthen the causal interpretation of these relationships. Using the establishment of big data comprehensive pilot zones as an identification strategy, recent research confirms that digital economy development promotes high-quality economic growth through human capital accumulation and green technological innovation, with pronounced heterogeneity across regions, city sizes, and development stages (). This strand of literature emphasizes that the impacts of the digital economy are mediated by internal mechanisms and conditioned by structural environments, rather than operating through simple linear effects.\n\nOverall, the existing evidence suggests that the digital economy is not merely a sector-specific technological upgrade, but a multidimensional development system capable of influencing economic performance, environmental sustainability, and social outcomes through resource reallocation, innovation diffusion, and institutional transformation. These cross-sectoral mechanisms, including resource reallocation, innovation diffusion, and institutional upgrading, are also highly relevant to agriculture, where factor mobility and technology adoption are central to green productivity improvements (; ; ).\n\n#### 2.1.2 Determinants of agricultural green total factor productivity\n\nAgricultural green total factor productivity (AGTFP) has been widely used as a comprehensive indicator to assess the quality and sustainability of agricultural development, as it simultaneously incorporates desirable outputs and undesirable environmental impacts such as carbon emissions and non-point source pollution (; ). A growing body of literature has examined the determinants of AGTFP, indicating that agricultural green productivity is jointly shaped by factor endowments and allocation efficiency, technological progress and diffusion, as well as institutional and environmental conditions.\n\nFirst, factor endowment structure and allocation efficiency constitute fundamental determinants of AGTFP. Existing studies suggest that labor input quality, capital accumulation, and land use efficiency are closely associated with agricultural green productivity (; ). In particular, distortions in factor allocation and barriers to factor mobility tend to reduce production efficiency while intensifying environmental pressures, thereby constraining improvements in AGTFP (). Optimizing the allocation of labor and capital across regions and sectors has therefore been identified as a critical pathway for enhancing agricultural green productivity.\n\nSecond, technological progress and the diffusion of agricultural technologies represent another key driver of AGTFP. Technological innovation not only improves production efficiency but also reduces resource consumption and pollution emissions through cleaner production methods and environmentally friendly input use (). Empirical evidence indicates that green technological innovation, mechanization, and the adoption of sustainable farming practices significantly enhance AGTFP; however, the effectiveness of technological progress depends critically on the speed and scope of technology diffusion, which varies across regions and stages of development (; ).\n\nThird, institutional arrangements, environmental regulations, and external conditions also exert significant influences on AGTFP. Climate change, environmental constraints, and policy interventions affect agricultural green productivity through both direct and indirect channels. For example, adverse climate conditions—such as rising temperatures and increased precipitation variability—have been shown to exert nonlinear and regionally heterogeneous effects on China’s AGTFP (). At the same time, appropriate environmental regulations and agricultural support policies can incentivize cleaner production behavior and stimulate green technological innovation, thereby contributing to improvements in agricultural green productivity (; ).\n\nOverall, the existing literature indicates that AGTFP is the outcome of multiple interacting forces, including factor allocation efficiency, technological progress and diffusion, and institutional and environmental conditions. This body of research provides a solid foundation for examining how new development forces may further influence agricultural green productivity and underscores the importance of exploring the mechanisms through which such influences operate.\n\n#### 2.1.3 Digital rural development and agricultural green total factor productivity\n\nBuilding on the established determinants of AGTFP discussed above, recent studies have begun to explore digital rural development as a new development force that reshapes these traditional drivers rather than replacing them (; ). As an extension of the digital economy into agriculture and rural areas, digital rural development integrates digital infrastructure, digital services, and supportive institutional environments, thereby transforming agricultural production conditions and rural factor allocation systems.\n\nExisting empirical evidence suggests that digitalization-related factors can significantly enhance AGTFP through multiple channels (; ; ; ; ). In particular, studies on digital inclusive finance show that improved access to digital financial services alleviates farmers’ credit constraints, promotes investment in green technologies, and enhances production efficiency, ultimately contributing to higher AGTFP (). Similarly, recent research demonstrates that digital finance promotes AGTFP indirectly by fostering digital village development, highlighting the mediating role of rural digital infrastructure and service capacity in transmitting digital dividends to agricultural green productivity ().\n\nBeyond financial digitalization, a growing strand of literature emphasizes the importance of comprehensive digital rural development in improving agricultural green productivity. Digital infrastructure, such as broadband networks and information platforms, reduces information asymmetry and transaction costs, thereby facilitating the more efficient allocation of labor, capital, and land across agricultural activities (; ; ). Meanwhile, enhanced digital service capacity improves farmers’ access to market information, technical guidance, and public services, which in turn promotes organizational upgrading and the adoption of environmentally friendly production practices.\n\nDigital rural development also plays a crucial role in accelerating the diffusion of green technologies and knowledge spillovers in agriculture. Through digital platforms and information networks, advanced agricultural technologies and sustainable farming practices can be disseminated more rapidly across regions, shortening the lag between innovation and adoption. This process not only enhances productivity but also mitigates environmental pressures by encouraging cleaner production methods and more efficient input use.\n\nTaken together, existing studies suggest that digital rural development affects AGTFP primarily by reshaping factor allocation, facilitating organizational upgrading, and accelerating the diffusion of green technologies, although the strength and timing of these effects depend on regional development conditions and infrastructure endowments. In regions with insufficient digital infrastructure or limited human capital, the positive impacts of digitalization on agricultural green productivity may be constrained or delayed, implying the presence of nonlinear characteristics and regional heterogeneity.\n\nDespite these contributions, existing studies exhibit several important limitations. First, much of the literature focuses on single dimensions of digitalization, such as digital finance, rather than conceptualizing digital rural development as an integrated system encompassing infrastructure, service capacity, and the broader development environment. Second, the internal mechanisms through which digital rural development influences AGTFP, particularly factor allocation, organizational upgrading, and technology diffusion, remain insufficiently examined. In addition, most existing studies rely on linear specifications and thus fail to capture potential threshold effects or stage-dependent dynamics, which may obscure the conditional nature of digital rural development’s impact on agricultural green productivity. In particular, when digital infrastructure and service capacity remain below critical levels, the enabling effects of digital rural development may not fully materialize. Accordingly, this study conceptualizes digital rural development as a systemic and conditional driver of agricultural green transformation rather than a technological input. By constructing a multidimensional digital rural development index and empirically examining its effects on AGTFP through mechanism analysis, threshold modeling, and regional heterogeneity analysis, this study seeks to extend the existing literature and provide more nuanced evidence on how digital transformation contributes to sustainable agricultural development.\n\n### 2.2 Theoretical analysis and research hypotheses\n\n#### 2.2.1 Overall effect of digital rural development on agricultural green total factor productivity\n\nDigital rural development—anchored in digital infrastructure, service capacity, and the development environment—facilitates the transformation and upgrading of agricultural production through multidimensional coordination, thereby enhancing agricultural green total factor productivity (AGTFP). The construction of digital infrastructure enhances information acquisition and transmission efficiency, reduces transaction costs in production and circulation, and improves resource utilization (; ). Enhanced digital service capacity allows farmers easier access to markets and policy resources, strengthening operational flexibility and supporting the transition toward precision and green production (). Meanwhile, a supportive digital development environment—characterized by stable energy supply, efficient transport, and industrial linkages—creates favorable conditions for applying digital tools and disseminating green practices (; ).\n\n> _H1_: Digital rural development significantly enhances agricultural green total factor productivity.\n\n#### 2.2.2 Indirect effects: mediation mechanisms\n\n##### 2.2.2.1 Factor allocation efficiency\n\nInstitutional economics posits that transaction costs and information asymmetry hinder the free flow of production factors (). By advancing information infrastructure and e-commerce platforms, digital rural development improves the allocation efficiency of land and labor. The expansion of internet access and digital platforms reduces transaction costs in land transfers (), promotes off-farm employment, and facilitates large-scale operations. Transparent digital information also alleviates asymmetry, improving market matching. Furthermore, digital platforms create new employment and training opportunities, optimizing labor allocation and human capital utilization, thereby enhancing resource efficiency and AGTFP.\n\n> _H2_: Digital rural development indirectly promotes AGTFP by improving the allocation efficiency of key factors such as land and labor.\n\n##### 2.2.2.2 Organizational upgrading\n\nAccording to collective action theory, the fragmented nature of smallholders hinders coordination in green agricultural production (). The rise of digital platforms fosters collaboration among cooperatives, family farms, agribusinesses, and individual farmers (,; ; ). These new organizational forms improve market access, information sharing, and resource integration while facilitating the collective adoption of green technologies and standardized production practices. Rural e-commerce cooperatives that leverage big data and logistics systems exemplify how digitalization enhances operational efficiency and reduces resource waste. Consequently, organizational upgrading strengthens specialization, fosters economies of scale, and accelerates the adoption of green technologies.\n\n> _H3_: Digital rural development indirectly promotes AGTFP by fostering organizational upgrading and large-scale agricultural operations.\n\n##### 2.2.2.3 Technology diffusion\n\nInnovation diffusion theory emphasizes that the adoption of new technologies depends on effective information channels and social acceptance (). Digital rural development accelerates the dissemination of green and smart agricultural technologies by enhancing connectivity and service systems. Supported by public policies and socialized service platforms, technologies such as IoT-based monitor","Frontiers in Sustainable Food Systems 2026-03-17","2026-03-17T00:00:00Z",74,{"impact":26,"substance":25,"depth":81,"authority":27,"freshness":113,"relevant":8,"comment":178},"基于省级面板数据的实证研究，揭示数字乡村对农业绿色全要素生产率的提升机制与区域异质性，结论可靠，但发表时间较早，时效性不足。",[180],{"name":174,"url":171},[32,182,183,4,184],"Frontiers","农业绿色全要素生产率","阈值模型",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":188},{"candidates":189,"agricultural_evidence":52},[190,194,200],{"code":39,"score":27,"supported":52,"title":191,"abstract":192,"tags":193},[32],[32],[32],{"code":195,"score":152,"supported":52,"title":196,"abstract":197,"tags":199},"F5",[],[198],"机械化",[],{"code":201,"score":152,"supported":52,"title":202,"abstract":203,"tags":205},"G2",[],[204],"土地流转",[],[207,208],"农业绿色全要素生产率 数字乡村 阈值模型 Frontiers","农业绿色全要素生产率 数字乡村","农业绿色全要素生产率数字乡村阈值模型Frontiers-222","10.3389\u002Ffsufs.2026.1746394\u002Ffull","2026-08-04T23:54:47.709265Z"]